# Project Overview

This repository contains the components required to run the code described in our research. The project is organized into several directories, each focusing on a specific part of the pipeline, along with sample data and detailed documentation.
We have provided an annonomous link to downlaod the weights https://mega.nz/folder/nzgGCTzI#m1wr5sm4JgR8iGlndlXfMA. Detailed instructions are in the in the sub folder README file.

## Architecture
The architecture directory contains pre-trained models saved in the pth folder. You will have complete access to the architecture used in the paper, including configurations and setup details. Additionally, we have provided sample code to calculate the Signal-to-Noise Ratio (SNR). Further information can be found in the directory's README file.

## SkinTone_Code

This folder includes the complete pipeline for generating skin tone data using the provided datasets. The pipeline uses FaceNet and Faster R-CNN (fingerphoto variant) to crop regions of interest (ROIs) for the face and finger. These cropped images are then processed to generate outputs such as skintone.csv and a directory of cropped images. **The outputs from this step will be used as inputs for other parts of the code**. Sample images are provided to test the code, with additional details available in the folder's README file.

## PCAAnalysis_Code

The PCA analysis directory contains code to perform Principal Component Analysis (PCA) on the skin tone data. To run this code, you must first execute the skin tone pipeline, as its output will be used as input for the PCA analysis. Ensure that you complete the skin tone code step before proceeding. Detailed instructions are provided in the folder's README file.

# Requirements and Documentation
Each directory includes a requirements.txt file that lists the necessary dependencies to run the code, as well as a dedicated README file with instructions specific to that component of the project. Please refer to these files for detailed setup and execution steps.